Using service graphs to compare performance of a plurality of versions of a microservice

ABSTRACT

Described embodiments provide systems and methods for using service graphs to compare performance of a plurality of versions of a microservice. A device may establish metrics from execution of a plurality of versions of a microservice of a service. The plurality of versions of the microservice are deployed concurrently for a portion of execution of the service. The device generates service graphs for each version of the plurality of versions of the microservice. The service graphs include metrics from monitoring execution of a respective version of the microservice. The device identifies differences in metrics between the service graphs for different versions of the microservice. The device requests a change in network traffic of the service between respective versions of the microservice based at least on the one or more differences.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. patentapplication Ser. No. 16/415,874, titled “USING SERVICE GRAPHS TO COMPAREPERFORMANCE OF A PLURITY OF VERSIONS OF A MICROSERVICE,” and filed onMay 17, 2019, the contents of all of which are hereby incorporatedherein by reference in its entirety for all purposes.

FIELD OF THE DISCLOSURE

The present application generally relates to service graphs, includingbut not limited to systems and methods for using a service graph of aplurality of microservices.

BACKGROUND

Various services may be used, accessed, or otherwise provided to users.Such services may include microservices which perform a subset of tasksor functions which, collectively, provide the service to the user.Various microservices and versions of microservices may be deployedunder different conditions, at different locations, handle differentamounts of traffic, etc.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features, nor is it intended to limit the scope of the claimsincluded herewith.

The present disclosure is directed to grouping microservices intonamespaces for generating service graphs of particular sets ofmicroservices. A service graph may be a tool by which a serviceincluding various microservices corresponding thereto may be visualized.Such a tool may be used for network traffic monitoring purposes,diagnostic purposes, troubleshooting purposes, and so forth. The servicegraph may depict various metrics corresponding to network conditions andtopology (e.g., traffic volume, latency, error rates, and other metricscorresponding to the service). In some implementations, such as duringrollout of a new version of a microservice, a user may want to monitorperformance of the new version of the microservice. For instance, theuser may want to monitor metrics of the new version of the microservicein comparison to the previous version of the microservice—particularlywhere the deployment of the new version is a canary deployment.

A device can monitor metrics from execution of a plurality of versionsof a microservice corresponding to a service. The versions may bedeployed concurrently for a portion of execution of the service (e.g.,as part of a canary deployment, for instance). The device can generateservice graphs of each version of the microservice which include themonitored metrics. The device can identify differences in metricsbetween the service graphs of the different versions. The device canrequest a change in network traffic of the service between the versionsof the microservice based on the identified differences in metrics. Assuch, the device can determine whether the deployment of a new versionof a microservice is performing as intended and gradually divert networktraffic to the new version while phasing out the previous version of themicroservice.

According to one aspect, this disclosure is directed to a method ofusing service graphs to compare performance of a plurality of versionsof a microservice. The method includes establishing, by one or moredevices, metrics from execution of a plurality of versions of amicroservice of a service deployed concurrently for at least a portionof execution of the service. The method includes generating, by the oneor more devices, a service graph for each version of the plurality ofversions of the microservice. Each of the service graphs include metricsfrom execution of a respective version of the microservice. The methodincludes identifying, by the one or more devices, one or moredifferences in metrics between the service graphs for different versionsof the microservice. The method includes requesting a change in networktraffic of the service between respective versions of the microservicebased at least on the one or more differences.

In some embodiments, the plurality of versions of the microservice aredeployed via a canary deployment. In some embodiments, establishingmetrics comprises distributing a first percentage of network traffic toa first version of the microservice and a second percentage of networktraffic to a second version of the microservice. In some embodiments,the method includes increasing over time the first percentage of networktraffic distributed to the first version of the microservice whiledecreasing the second percentage of the network traffic distributed tothe second version of the microservice.

In some embodiments, the method further includes generating, by the oneor more devices, the service graphs to comprise an arc between aplurality of microservices of the service. The arc may identify one ormore metrics. In some embodiments, the arc identifies traffic volumebetween at least two microservices of the plurality of microservices. Insome embodiments, the arc identifies latency between at least twomicroservices of the plurality of microservices. In some embodiments,the arc identifies an error rate between at least two microservices ofthe plurality of microservices. In some embodiments, the arc isconnected between a first node representing a state of the microserviceand a second node representing a state of a second microservice. In someembodiments, requesting the change includes requesting to switch atleast a portion of network traffic from one version of the microserviceto another version of the microservice based on the one or moredifferences.

According to another aspect, this disclosure is directed to a system ofusing service graphs to compare performance of a plurality of versionsof a microservice. The system includes one or more devices comprisingone or more processors coupled to memory and configured to establishmetrics from execution of a plurality of versions of a microservice of aservice deployed concurrently for at least a portion of execution of theservice. The one or more devices are configured to generate servicegraph for each version of the plurality of versions of the microservice.Each of the service graphs include metrics from execution of arespective version of the microservice. The one or more devices areconfigured to identify one or more differences in metrics between theservice graphs. The devices are configured to request a change innetwork traffic of the service between respective versions of themicroservice based at least on the one or more differences.

In some embodiments, the plurality of versions of the microservice aredeployed via a canary deployment. In some embodiments, a firstpercentage of network traffic is distributed to a first version of themicroservice and a second percentage of network traffic to a secondversion of the microservice. In some embodiments, over time the firstpercentage of network traffic distributed to the first version of themicroservice is increased while the second percentage of the networktraffic distributed to the second version of the microservice isdecreased.

In some embodiments, the one or more devices are configured to generatethe service graphs to comprise an arc between a plurality ofmicroservices of the service. The arc may identify one or more metrics.In some embodiments, the arc identifies traffic volume between at leasttwo microservices of the plurality of microservices. In someembodiments, the arc identifies latency between at least twomicroservices of the plurality of microservices. In some embodiments,the arc identifies an error rate between at least two microservices ofthe plurality of microservices. In some embodiments, the arc isconnected between a first node representing a state of the microserviceand a second node representing a state of a second microservice. In someembodiments, the one or more devices are configured to switch at least aportion of network traffic from one version of the microservice toanother version of the microservice based on the one or moredifferences.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Objects, aspects, features, and advantages of embodiments disclosedherein will become more fully apparent from the following detaileddescription, the appended claims, and the accompanying drawing figuresin which like reference numerals identify similar or identical elements.Reference numerals that are introduced in the specification inassociation with a drawing figure may be repeated in one or moresubsequent figures without additional description in the specificationin order to provide context for other features, and not every elementmay be labeled in every figure. The drawing figures are not necessarilyto scale, emphasis instead being placed upon illustrating embodiments,principles and concepts. The drawings are not intended to limit thescope of the claims included herewith.

FIG. 1A is a block diagram of a network computing system, in accordancewith an illustrative embodiment;

FIG. 1B is a block diagram of a network computing system for deliveringa computing environment from a server to a client via an appliance, inaccordance with an illustrative embodiment;

FIG. 1C is a block diagram of a computing device, in accordance with anillustrative embodiment;

FIG. 2 is a block diagram of an appliance for processing communicationsbetween a client and a server, in accordance with an illustrativeembodiment;

FIG. 3 is a block diagram of a virtualization environment, in accordancewith an illustrative embodiment;

FIG. 4 is a block diagram of a cluster system, in accordance with anillustrative embodiment;

FIG. 5A is a block diagram of a service graph based system, inaccordance with an illustrative embodiment;

FIG. 5B is a block diagram of a service graph, in accordance with anillustrative embodiment;

FIG. 5C is a flow diagram of a method of using a service graph, inaccordance with an illustrative embodiment;

FIG. 6A is an example service graph including two versions of amicroservice, in accordance with an illustrative embodiment;

FIG. 6B is an example service graph including a first version of amicroservice, in accordance with an illustrative embodiment;

FIG. 6C is an example service graph including a second version of amicroservice, in accordance with an illustrative embodiment; and

FIG. 6D is a flow diagram of a method for using service graphs tocompare performance of a plurality of versions of a microservice, inaccordance with an illustrative embodiment.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodimentsbelow, the following descriptions of the sections of the specificationand their respective contents may be helpful:

Section A describes a network environment and computing environmentwhich may be useful for practicing embodiments described herein;

Section B describes embodiments of systems and methods for delivering acomputing environment to a remote user;

Section C describes embodiments of systems and methods for virtualizingan application delivery controller;

Section D describes embodiments of systems and methods for providing aclustered appliance architecture environment;

Section E describes embodiments of a service graph based platform andtechnology; and

Section F describes embodiments of systems and methods for using servicegraphs to compare performance of a plurality of versions of amicroservice

A. Network and Computing Environment

Referring to FIG. 1A, an illustrative network environment 100 isdepicted. Network environment 100 may include one or more clients102(1)-102(n) (also generally referred to as local machine(s) 102 orclient(s) 102) in communication with one or more servers 106(1)-106(n)(also generally referred to as remote machine(s) 106 or server(s) 106)via one or more networks 104(1)-104 n (generally referred to asnetwork(s) 104). In some embodiments, a client 102 may communicate witha server 106 via one or more appliances 200(1)-200 n (generally referredto as appliance(s) 200 or gateway(s) 200).

Although the embodiment shown in FIG. 1A shows one or more networks 104between clients 102 and servers 106, in other embodiments, clients 102and servers 106 may be on the same network 104. The various networks 104may be the same type of network or different types of networks. Forexample, in some embodiments, network 104(1) may be a private networksuch as a local area network (LAN) or a company Intranet, while network104(2) and/or network 104(n) may be a public network, such as a widearea network (WAN) or the Internet. In other embodiments, both network104(1) and network 104(n) may be private networks. Networks 104 mayemploy one or more types of physical networks and/or network topologies,such as wired and/or wireless networks, and may employ one or morecommunication transport protocols, such as transmission control protocol(TCP), internet protocol (IP), user datagram protocol (UDP) or othersimilar protocols.

As shown in FIG. 1A, one or more appliances 200 may be located atvarious points or in various communication paths of network environment100. For example, appliance 200 may be deployed between two networks104(1) and 104(2), and appliances 200 may communicate with one anotherto work in conjunction to, for example, accelerate network trafficbetween clients 102 and servers 106. In other embodiments, the appliance200 may be located on a network 104. For example, appliance 200 may beimplemented as part of one of clients 102 and/or servers 106. In anembodiment, appliance 200 may be implemented as a network device such asCitrix networking (formerly NetScaler®) products sold by Citrix Systems,Inc. of Fort Lauderdale, Fla.

As shown in FIG. 1A, one or more servers 106 may operate as a serverfarm 38. Servers 106 of server farm 38 may be logically grouped, and mayeither be geographically co-located (e.g., on premises) orgeographically dispersed (e.g., cloud based) from clients 102 and/orother servers 106. In an embodiment, server farm 38 executes one or moreapplications on behalf of one or more of clients 102 (e.g., as anapplication server), although other uses are possible, such as a fileserver, gateway server, proxy server, or other similar server uses.Clients 102 may seek access to hosted applications on servers 106.

As shown in FIG. 1A, in some embodiments, appliances 200 may include, bereplaced by, or be in communication with, one or more additionalappliances, such as WAN optimization appliances 205(1)-205(n), referredto generally as WAN optimization appliance(s) 205. For example, WANoptimization appliance 205 may accelerate, cache, compress or otherwiseoptimize or improve performance, operation, flow control, or quality ofservice of network traffic, such as traffic to and/or from a WANconnection, such as optimizing Wide Area File Services (WAFS),accelerating Server Message Block (SMB) or Common Internet File System(CIFS). In some embodiments, appliance 205 may be a performanceenhancing proxy or a WAN optimization controller. In one embodiment,appliance 205 may be implemented as Citrix SD-WAN products sold byCitrix Systems, Inc. of Fort Lauderdale, Fla.

Referring to FIG. 1B, an example network environment, 100′, fordelivering and/or operating a computing network environment on a client102 is shown. As shown in FIG. 1B, a server 106 may include anapplication delivery system 190 for delivering a computing environment,application, and/or data files to one or more clients 102. Client 102may include client agent 120 and computing environment 15. Computingenvironment 15 may execute or operate an application, 16, that accesses,processes or uses a data file 17. Computing environment 15, application16 and/or data file 17 may be delivered via appliance 200 and/or theserver 106.

Appliance 200 may accelerate delivery of all or a portion of computingenvironment 15 to a client 102, for example by the application deliverysystem 190. For example, appliance 200 may accelerate delivery of astreaming application and data file processable by the application froma data center to a remote user location by accelerating transport layertraffic between a client 102 and a server 106. Such acceleration may beprovided by one or more techniques, such as: 1) transport layerconnection pooling, 2) transport layer connection multiplexing, 3)transport control protocol buffering, 4) compression, 5) caching, orother techniques. Appliance 200 may also provide load balancing ofservers 106 to process requests from clients 102, act as a proxy oraccess server to provide access to the one or more servers 106, providesecurity and/or act as a firewall between a client 102 and a server 106,provide Domain Name Service (DNS) resolution, provide one or morevirtual servers or virtual internet protocol servers, and/or provide asecure virtual private network (VPN) connection from a client 102 to aserver 106, such as a secure socket layer (SSL) VPN connection and/orprovide encryption and decryption operations.

Application delivery management system 190 may deliver computingenvironment 15 to a user (e.g., client 102), remote or otherwise, basedon authentication and authorization policies applied by policy engine195. A remote user may obtain a computing environment and access toserver stored applications and data files from any network-connecteddevice (e.g., client 102). For example, appliance 200 may request anapplication and data file from server 106. In response to the request,application delivery system 190 and/or server 106 may deliver theapplication and data file to client 102, for example via an applicationstream to operate in computing environment 15 on client 102, or via aremote-display protocol or otherwise via remote-based or server-basedcomputing. In an embodiment, application delivery system 190 may beimplemented as any portion of the Citrix Workspace Suite™ by CitrixSystems, Inc., such as Citrix Virtual Apps and Desktops (formerlyXenApp® and XenDesktop®).

Policy engine 195 may control and manage the access to, and executionand delivery of, applications. For example, policy engine 195 maydetermine the one or more applications a user or client 102 may accessand/or how the application should be delivered to the user or client102, such as a server-based computing, streaming or delivering theapplication locally to the client 120 for local execution.

For example, in operation, a client 102 may request execution of anapplication (e.g., application 16′) and application delivery system 190of server 106 determines how to execute application 16′, for examplebased upon credentials received from client 102 and a user policyapplied by policy engine 195 associated with the credentials. Forexample, application delivery system 190 may enable client 102 toreceive application-output data generated by execution of theapplication on a server 106, may enable client 102 to execute theapplication locally after receiving the application from server 106, ormay stream the application via network 104 to client 102. For example,in some embodiments, the application may be a server-based or aremote-based application executed on server 106 on behalf of client 102.Server 106 may display output to client 102 using a thin-client orremote-display protocol, such as the Independent Computing Architecture(ICA) protocol by Citrix Systems, Inc. of Fort Lauderdale, Fla. Theapplication may be any application related to real-time datacommunications, such as applications for streaming graphics, streamingvideo and/or audio or other data, delivery of remote desktops orworkspaces or hosted services or applications, for exampleinfrastructure as a service (IaaS), desktop as a service (DaaS),workspace as a service (WaaS), software as a service (SaaS) or platformas a service (PaaS).

One or more of servers 106 may include a performance monitoring serviceor agent 197. In some embodiments, a dedicated one or more servers 106may be employed to perform performance monitoring. Performancemonitoring may be performed using data collection, aggregation,analysis, management and reporting, for example by software, hardware ora combination thereof. Performance monitoring may include one or moreagents for performing monitoring, measurement and data collectionactivities on clients 102 (e.g., client agent 120), servers 106 (e.g.,agent 197) or an appliance 200 and/or 205 (agent not shown). In general,monitoring agents (e.g., 120 and/or 197) execute transparently (e.g., inthe background) to any application and/or user of the device. In someembodiments, monitoring agent 197 includes any of the productembodiments referred to as Citrix Analytics or Citrix ApplicationDelivery Management by Citrix Systems, Inc. of Fort Lauderdale, Fla.

The monitoring agents 120 and 197 may monitor, measure, collect, and/oranalyze data on a predetermined frequency, based upon an occurrence ofgiven event(s), or in real time during operation of network environment100. The monitoring agents may monitor resource consumption and/orperformance of hardware, software, and/or communications resources ofclients 102, networks 104, appliances 200 and/or 205, and/or servers106. For example, network connections such as a transport layerconnection, network latency, bandwidth utilization, end-user responsetimes, application usage and performance, session connections to anapplication, cache usage, memory usage, processor usage, storage usage,database transactions, client and/or server utilization, active users,duration of user activity, application crashes, errors, or hangs, thetime required to log-in to an application, a server, or the applicationdelivery system, and/or other performance conditions and metrics may bemonitored.

The monitoring agents 120 and 197 may provide application performancemanagement for application delivery system 190. For example, based uponone or more monitored performance conditions or metrics, applicationdelivery system 190 may be dynamically adjusted, for exampleperiodically or in real-time, to optimize application delivery byservers 106 to clients 102 based upon network environment performanceand conditions.

In described embodiments, clients 102, servers 106, and appliances 200and 205 may be deployed as and/or executed on any type and form ofcomputing device, such as any desktop computer, laptop computer, ormobile device capable of communication over at least one network andperforming the operations described herein. For example, clients 102,servers 106 and/or appliances 200 and 205 may each correspond to onecomputer, a plurality of computers, or a network of distributedcomputers such as computer 101 shown in FIG. 1C.

As shown in FIG. 1C, computer 101 may include one or more processors103, volatile memory 122 (e.g., RAM), non-volatile memory 128 (e.g., oneor more hard disk drives (HDDs) or other magnetic or optical storagemedia, one or more solid state drives (SSDs) such as a flash drive orother solid state storage media, one or more hybrid magnetic and solidstate drives, and/or one or more virtual storage volumes, such as acloud storage, or a combination of such physical storage volumes andvirtual storage volumes or arrays thereof), user interface (UI) 123, oneor more communications interfaces 118, and communication bus 150. Userinterface 123 may include graphical user interface (GUI) 124 (e.g., atouchscreen, a display, etc.) and one or more input/output (I/O) devices126 (e.g., a mouse, a keyboard, etc.). Non-volatile memory 128 storesoperating system 115, one or more applications 116, and data 117 suchthat, for example, computer instructions of operating system 115 and/orapplications 116 are executed by processor(s) 103 out of volatile memory122. Data may be entered using an input device of GUI 124 or receivedfrom I/O device(s) 126. Various elements of computer 101 may communicatevia communication bus 150. Computer 101 as shown in FIG. 1C is shownmerely as an example, as clients 102, servers 106 and/or appliances 200and 205 may be implemented by any computing or processing environmentand with any type of machine or set of machines that may have suitablehardware and/or software capable of operating as described herein.

Processor(s) 103 may be implemented by one or more programmableprocessors executing one or more computer programs to perform thefunctions of the system. As used herein, the term “processor” describesan electronic circuit that performs a function, an operation, or asequence of operations. The function, operation, or sequence ofoperations may be hard coded into the electronic circuit or soft codedby way of instructions held in a memory device. A “processor” mayperform the function, operation, or sequence of operations using digitalvalues or using analog signals. In some embodiments, the “processor” canbe embodied in one or more application specific integrated circuits(ASICs), microprocessors, digital signal processors, microcontrollers,field programmable gate arrays (FPGAs), programmable logic arrays(PLAs), multi-core processors, or general-purpose computers withassociated memory. The “processor” may be analog, digital ormixed-signal. In some embodiments, the “processor” may be one or morephysical processors or one or more “virtual” (e.g., remotely located or“cloud”) processors.

Communications interfaces 118 may include one or more interfaces toenable computer 101 to access a computer network such as a LAN, a WAN,or the Internet through a variety of wired and/or wireless or cellularconnections.

In described embodiments, a first computing device 101 may execute anapplication on behalf of a user of a client computing device (e.g., aclient 102), may execute a virtual machine, which provides an executionsession within which applications execute on behalf of a user or aclient computing device (e.g., a client 102), such as a hosted desktopsession, may execute a terminal services session to provide a hosteddesktop environment, or may provide access to a computing environmentincluding one or more of: one or more applications, one or more desktopapplications, and one or more desktop sessions in which one or moreapplications may execute.

B. Appliance Architecture

FIG. 2 shows an example embodiment of appliance 200. As describedherein, appliance 200 may be implemented as a server, gateway, router,switch, bridge or other type of computing or network device. As shown inFIG. 2, an embodiment of appliance 200 may include a hardware layer 206and a software layer 205 divided into a user space 202 and a kernelspace 204. Hardware layer 206 provides the hardware elements upon whichprograms and services within kernel space 204 and user space 202 areexecuted and allow programs and services within kernel space 204 anduser space 202 to communicate data both internally and externally withrespect to appliance 200. As shown in FIG. 2, hardware layer 206 mayinclude one or more processing units 262 for executing software programsand services, memory 264 for storing software and data, network ports266 for transmitting and receiving data over a network, and encryptionprocessor 260 for encrypting and decrypting data such as in relation toSecure Socket Layer (SSL) or Transport Layer Security (TLS) processingof data transmitted and received over the network.

An operating system of appliance 200 allocates, manages, or otherwisesegregates the available system memory into kernel space 204 and userspace 202. Kernel space 204 is reserved for running kernel 230,including any device drivers, kernel extensions or other kernel relatedsoftware. As known to those skilled in the art, kernel 230 is the coreof the operating system, and provides access, control, and management ofresources and hardware-related elements of application 104. Kernel space204 may also include a number of network services or processes workingin conjunction with cache manager 232.

Appliance 200 may include one or more network stacks 267, such as aTCP/IP based stack, for communicating with client(s) 102, server(s) 106,network(s) 104, and/or other appliances 200 or 205. For example,appliance 200 may establish and/or terminate one or more transport layerconnections between clients 102 and servers 106. Each network stack 267may include a buffer 243 for queuing one or more network packets fortransmission by appliance 200.

Kernel space 204 may include cache manager 232, packet engine 240,encryption engine 234, policy engine 236 and compression engine 238. Inother words, one or more of processes 232, 240, 234, 236 and 238 run inthe core address space of the operating system of appliance 200, whichmay reduce the number of data transactions to and from the memory and/orcontext switches between kernel mode and user mode, for example sincedata obtained in kernel mode may not need to be passed or copied to auser process, thread or user level data structure.

Cache manager 232 may duplicate original data stored elsewhere or datapreviously computed, generated or transmitted to reducing the accesstime of the data. In some embodiments, the cache memory may be a dataobject in memory 264 of appliance 200, or may be a physical memoryhaving a faster access time than memory 264.

Policy engine 236 may include a statistical engine or otherconfiguration mechanism to allow a user to identify, specify, define orconfigure a caching policy and access, control and management ofobjects, data or content being cached by appliance 200, and define orconfigure security, network traffic, network access, compression orother functions performed by appliance 200.

Encryption engine 234 may process any security related protocol, such asSSL or TLS. For example, encryption engine 234 may encrypt and decryptnetwork packets, or any portion thereof, communicated via appliance 200,may setup or establish SSL, TLS or other secure connections, for examplebetween client 102, server 106, and/or other appliances 200 or 205. Insome embodiments, encryption engine 234 may use a tunneling protocol toprovide a VPN between a client 102 and a server 106. In someembodiments, encryption engine 234 is in communication with encryptionprocessor 260. Compression engine 238 compresses network packetsbi-directionally between clients 102 and servers 106 and/or between oneor more appliances 200.

Packet engine 240 may manage kernel-level processing of packets receivedand transmitted by appliance 200 via network stacks 267 to send andreceive network packets via network ports 266. Packet engine 240 mayoperate in conjunction with encryption engine 234, cache manager 232,policy engine 236 and compression engine 238, for example to performencryption/decryption, traffic management such as request-level contentswitching and request-level cache redirection, and compression anddecompression of data.

User space 202 is a memory area or portion of the operating system usedby user mode applications or programs otherwise running in user mode. Auser mode application may not access kernel space 204 directly and usesservice calls in order to access kernel services. User space 202 mayinclude graphical user interface (GUI) 210, a command line interface(CLI) 212, shell services 214, health monitor 216, and daemon services218. GUI 210 and CLI 212 enable a system administrator or other user tointeract with and control the operation of appliance 200, such as viathe operating system of appliance 200. Shell services 214 include theprograms, services, tasks, processes or executable instructions tosupport interaction with appliance 200 by a user via the GUI 210 and/orCLI 212.

Health monitor 216 monitors, checks, reports and ensures that networksystems are functioning properly and that users are receiving requestedcontent over a network, for example by monitoring activity of appliance200. In some embodiments, health monitor 216 intercepts and inspects anynetwork traffic passed via appliance 200. For example, health monitor216 may interface with one or more of encryption engine 234, cachemanager 232, policy engine 236, compression engine 238, packet engine240, daemon services 218, and shell services 214 to determine a state,status, operating condition, or health of any portion of the appliance200. Further, health monitor 216 may determine if a program, process,service or task is active and currently running, check status, error orhistory logs provided by any program, process, service or task todetermine any condition, status or error with any portion of appliance200. Additionally, health monitor 216 may measure and monitor theperformance of any application, program, process, service, task orthread executing on appliance 200.

Daemon services 218 are programs that run continuously or in thebackground and handle periodic service requests received by appliance200. In some embodiments, a daemon service may forward the requests toother programs or processes, such as another daemon service 218 asappropriate.

As described herein, appliance 200 may relieve servers 106 of much ofthe processing load caused by repeatedly opening and closing transportlayer connections to clients 102 by opening one or more transport layerconnections with each server 106 and maintaining these connections toallow repeated data accesses by clients via the Internet (e.g.,“connection pooling”). To perform connection pooling, appliance 200 maytranslate or multiplex communications by modifying sequence numbers andacknowledgment numbers at the transport layer protocol level (e.g.,“connection multiplexing”). Appliance 200 may also provide switching orload balancing for communications between the client 102 and server 106.

As described herein, each client 102 may include client agent 120 forestablishing and exchanging communications with appliance 200 and/orserver 106 via a network 104. Client 102 may have installed and/orexecute one or more applications that are in communication with network104. Client agent 120 may intercept network communications from anetwork stack used by the one or more applications. For example, clientagent 120 may intercept a network communication at any point in anetwork stack and redirect the network communication to a destinationdesired, managed or controlled by client agent 120, for example tointercept and redirect a transport layer connection to an IP address andport controlled or managed by client agent 120. Thus, client agent 120may transparently intercept any protocol layer below the transportlayer, such as the network layer, and any protocol layer above thetransport layer, such as the session, presentation or applicationlayers. Client agent 120 can interface with the transport layer tosecure, optimize, accelerate, route or load-balance any communicationsprovided via any protocol carried by the transport layer.

In some embodiments, client agent 120 is implemented as an IndependentComputing Architecture (ICA) client developed by Citrix Systems, Inc. ofFort Lauderdale, Fla. Client agent 120 may perform acceleration,streaming, monitoring, and/or other operations. For example, clientagent 120 may accelerate streaming an application from a server 106 to aclient 102. Client agent 120 may also perform end-pointdetection/scanning and collect end-point information about client 102for appliance 200 and/or server 106. Appliance 200 and/or server 106 mayuse the collected information to determine and provide access,authentication and authorization control of the client's connection tonetwork 104. For example, client agent 120 may identify and determineone or more client-side attributes, such as: the operating system and/ora version of an operating system, a service pack of the operatingsystem, a running service, a running process, a file, presence orversions of various applications of the client, such as antivirus,firewall, security, and/or other software.

C. Systems and Methods for Providing Virtualized Application DeliveryController

Referring now to FIG. 3, a block diagram of a virtualized environment300 is shown. As shown, a computing device 302 in virtualizedenvironment 300 includes a virtualization layer 303, a hypervisor layer304, and a hardware layer 307. Hypervisor layer 304 includes one or morehypervisors (or virtualization managers) 301 that allocates and managesaccess to a number of physical resources in hardware layer 307 (e.g.,physical processor(s) 321 and physical disk(s) 328) by at least onevirtual machine (VM) (e.g., one of VMs 306) executing in virtualizationlayer 303. Each VM 306 may include allocated virtual resources such asvirtual processors 332 and/or virtual disks 342, as well as virtualresources such as virtual memory and virtual network interfaces. In someembodiments, at least one of VMs 306 may include a control operatingsystem (e.g., 305) in communication with hypervisor 301 and used toexecute applications for managing and configuring other VMs (e.g., guestoperating systems 310) on device 302.

In general, hypervisor(s) 301 may provide virtual resources to anoperating system of VMs 306 in any manner that simulates the operatingsystem having access to a physical device. Thus, hypervisor(s) 301 maybe used to emulate virtual hardware, partition physical hardware,virtualize physical hardware, and execute virtual machines that provideaccess to computing environments. In an illustrative embodiment,hypervisor(s) 301 may be implemented as a Citrix Hypervisor by CitrixSystems, Inc. of Fort Lauderdale, Fla. In an illustrative embodiment,device 302 executing a hypervisor that creates a virtual machineplatform on which guest operating systems may execute is referred to asa host server. 302

Hypervisor 301 may create one or more VMs 306 in which an operatingsystem (e.g., control operating system 305 and/or guest operating system310) executes. For example, the hypervisor 301 loads a virtual machineimage to create VMs 306 to execute an operating system. Hypervisor 301may present VMs 306 with an abstraction of hardware layer 307, and/ormay control how physical capabilities of hardware layer 307 arepresented to VMs 306. For example, hypervisor(s) 301 may manage a poolof resources distributed across multiple physical computing devices.

In some embodiments, one of VMs 306 (e.g., the VM executing controloperating system 305) may manage and configure other of VMs 306, forexample by managing the execution and/or termination of a VM and/ormanaging allocation of virtual resources to a VM. In variousembodiments, VMs may communicate with hypervisor(s) 301 and/or other VMsvia, for example, one or more Application Programming Interfaces (APIs),shared memory, and/or other techniques.

In general, VMs 306 may provide a user of device 302 with access toresources within virtualized computing environment 300, for example, oneor more programs, applications, documents, files, desktop and/orcomputing environments, or other resources. In some embodiments, VMs 306may be implemented as fully virtualized VMs that are not aware that theyare virtual machines (e.g., a Hardware Virtual Machine or HVM). In otherembodiments, the VM may be aware that it is a virtual machine, and/orthe VM may be implemented as a paravirtualized (PV) VM.

Although shown in FIG. 3 as including a single virtualized device 302,virtualized environment 300 may include a plurality of networked devicesin a system in which at least one physical host executes a virtualmachine. A device on which a VM executes may be referred to as aphysical host and/or a host machine. For example, appliance 200 may beadditionally or alternatively implemented in a virtualized environment300 on any computing device, such as a client 102, server 106 orappliance 200. Virtual appliances may provide functionality foravailability, performance, health monitoring, caching and compression,connection multiplexing and pooling and/or security processing (e.g.,firewall, VPN, encryption/decryption, etc.), similarly as described inregard to appliance 200.

In some embodiments, a server may execute multiple virtual machines 306,for example on various cores of a multi-core processing system and/orvarious processors of a multiple processor device. For example, althoughgenerally shown herein as “processors” (e.g., in FIGS. 1C, 2 and 3), oneor more of the processors may be implemented as either single- ormulti-core processors to provide a multi-threaded, parallel architectureand/or multi-core architecture. Each processor and/or core may have oruse memory that is allocated or assigned for private or local use thatis only accessible by that processor/core, and/or may have or use memorythat is public or shared and accessible by multiple processors/cores.Such architectures may allow work, task, load or network trafficdistribution across one or more processors and/or one or more cores(e.g., by functional parallelism, data parallelism, flow-based dataparallelism, etc.).

Further, instead of (or in addition to) the functionality of the coresbeing implemented in the form of a physical processor/core, suchfunctionality may be implemented in a virtualized environment (e.g.,300) on a client 102, server 106 or appliance 200, such that thefunctionality may be implemented across multiple devices, such as acluster of computing devices, a server farm or network of computingdevices, etc. The various processors/cores may interface or communicatewith each other using a variety of interface techniques, such as core tocore messaging, shared memory, kernel APIs, etc.

In embodiments employing multiple processors and/or multiple processorcores, described embodiments may distribute data packets among cores orprocessors, for example to balance the flows across the cores. Forexample, packet distribution may be based upon determinations offunctions performed by each core, source and destination addresses,and/or whether: a load on the associated core is above a predeterminedthreshold; the load on the associated core is below a predeterminedthreshold; the load on the associated core is less than the load on theother cores; or any other metric that can be used to determine where toforward data packets based in part on the amount of load on a processor.

For example, data packets may be distributed among cores or processesusing receive-side scaling (RSS) in order to process packets usingmultiple processors/cores in a network. RSS generally allows packetprocessing to be balanced across multiple processors/cores whilemaintaining in-order delivery of the packets. In some embodiments, RSSmay use a hashing scheme to determine a core or processor for processinga packet.

The RSS may generate hashes from any type and form of input, such as asequence of values. This sequence of values can include any portion ofthe network packet, such as any header, field or payload of networkpacket, and include any tuples of information associated with a networkpacket or data flow, such as addresses and ports. The hash result or anyportion thereof may be used to identify a processor, core, engine, etc.,for distributing a network packet, for example via a hash table,indirection table, or other mapping technique.

D. Systems and Methods for Providing a Distributed Cluster Architecture

Although shown in FIGS. 1A and 1B as being single appliances, appliances200 may be implemented as one or more distributed or clusteredappliances. Individual computing devices or appliances may be referredto as nodes of the cluster. A centralized management system may performload balancing, distribution, configuration, or other tasks to allow thenodes to operate in conjunction as a single computing system. Such acluster may be viewed as a single virtual appliance or computing device.FIG. 4 shows a block diagram of an illustrative computing device clusteror appliance cluster 400. A plurality of appliances 200 or othercomputing devices (e.g., nodes) may be joined into a single cluster 400.Cluster 400 may operate as an application server, network storageserver, backup service, or any other type of computing device to performmany of the functions of appliances 200 and/or 205.

In some embodiments, each appliance 200 of cluster 400 may beimplemented as a multi-processor and/or multi-core appliance, asdescribed herein. Such embodiments may employ a two-tier distributionsystem, with one appliance if the cluster distributing packets to nodesof the cluster, and each node distributing packets for processing toprocessors/cores of the node. In many embodiments, one or more ofappliances 200 of cluster 400 may be physically grouped orgeographically proximate to one another, such as a group of bladeservers or rack mount devices in a given chassis, rack, and/or datacenter. In some embodiments, one or more of appliances 200 of cluster400 may be geographically distributed, with appliances 200 notphysically or geographically co-located. In such embodiments,geographically remote appliances may be joined by a dedicated networkconnection and/or VPN. In geographically distributed embodiments, loadbalancing may also account for communications latency betweengeographically remote appliances.

In some embodiments, cluster 400 may be considered a virtual appliance,grouped via common configuration, management, and purpose, rather thanas a physical group. For example, an appliance cluster may comprise aplurality of virtual machines or processes executed by one or moreservers.

As shown in FIG. 4, appliance cluster 400 may be coupled to a firstnetwork 104(1) via client data plane 402, for example to transfer databetween clients 102 and appliance cluster 400. Client data plane 402 maybe implemented a switch, hub, router, or other similar network deviceinternal or external to cluster 400 to distribute traffic across thenodes of cluster 400. For example, traffic distribution may be performedbased on equal-cost multi-path (ECMP) routing with next hops configuredwith appliances or nodes of the cluster, open-shortest path first(OSPF), stateless hash-based traffic distribution, link aggregation(LAG) protocols, or any other type and form of flow distribution, loadbalancing, and routing.

Appliance cluster 400 may be coupled to a second network 104(2) viaserver data plane 404. Similarly to client data plane 402, server dataplane 404 may be implemented as a switch, hub, router, or other networkdevice that may be internal or external to cluster 400. In someembodiments, client data plane 402 and server data plane 404 may bemerged or combined into a single device.

In some embodiments, each appliance 200 of cluster 400 may be connectedvia an internal communication network or back plane 406. Back plane 406may enable inter-node or inter-appliance control and configurationmessages, for inter-node forwarding of traffic, and/or for communicatingconfiguration and control traffic from an administrator or user tocluster 400. In some embodiments, back plane 406 may be a physicalnetwork, a VPN or tunnel, or a combination thereof.

E. Service Graph Based Platform and Technology

Referring now to FIGS. 5A-5C, implementation of systems and methods fora service graph based platform and technology will be discussed. Aservice graph is a useful technology tool for visualizing a service byits topology of components and network elements. Services may be made upof microservices with each microservice handling a particular set of oneor more functions of the service. Network traffic may traverse theservice topology such as a client communicating with a server to accessservice (e.g., north-south traffic). Network traffic of a service mayinclude network traffic communicated between microservices of theservices such as within a data center or between data centers (e.g.,east-west traffic). The service graph may be used to identify andprovide metrics of such network traffic of the service as well asoperation and performance of any network elements used to provide theservice. Service graphs may be used for identifying and determiningissues with the service and which part of the topology causing theissue. Services graphs may be used to provide for administering,managing and configuring of services to improve operational performanceof such services.

Referring to FIG. 5A, an implementation of a system for service graphs,such as those illustrated in FIG. 5B, will be described. A device on anetwork, such as a network device 200, 205 or a server 206, may includea service graph generator and configurator 512, a service graph display514 and service graph monitor 516. The service graph generator andconfigurator 512 (generally referred to as service graph generator 512),may identify a topology 510 of elements in the network and metrics 518related to the network and the elements, to generate and/or configureservice graphs 505A-N. The service graphs 505A-N(generally referred toas service graphs 505) may be stored in one or more databases, with anyof the metric 518′ and/or topology 510′. The service graphic generator512 may generate data of the service graphs 505 to be displayed in adisplay or rendered form such as via a user interface, generatedreferred to as service graph display 514. Service graph monitor 516 maymonitor the network elements of the topology and service for metrics 518to configure and generate a service graph 505 and/or to updatedynamically or in real-time the elements and metrics 518 of orrepresented by a service graph display 514.

The topology 510 may include data identifying, describing, specifying orotherwise representing any elements used, traversed in accessing any oneor more services or otherwise included with or part of such one or moreservices, such as any of the services 275 described herein. The topologymay include data identifying or describing any one or more networks andnetwork elements traversed to access or use the services, including anynetwork devices, routers, switches, gateways, proxies, appliances,network connections or links, Internet Service Providers (ISPs), etc.The topology may include data identifying or describing any one or moreapplications, software, programs, services, processes, tasks orfunctions that are used or traversed in accessing a service. In someimplementations, a service may be made up or include multiplemicroservices, each providing one or more functions, functionality oroperations of or for a service. The topology may include dataidentifying or describing any one or more components of a service, suchas programs, functions, applications or microservices used to providethe service. The topology may include parameters, configuration dataand/or metadata about any portion of the topology, such as any elementof the topology.

A service graph 505 may include data representing the topology of aservice 275, such any elements making up such a service or used by theservice, for example as illustrated in FIG. 5B. The service graph may bein a node base form, such as graphical form of nodes and each noderepresenting an element or function of the topology of the service. Aservice graph may represent the topology of a service using nodesconnected among each other via various connectors or links, which may bereferred to as arcs. The arc may identify a relationship betweenelements connected by the arc. Nodes and arcs may be arranged in amanner to identify or describe one or more services. Nodes and arcs maybe arranged in a manner to identify or describe functions provided bythe one or more services. For example, a function node may represent afunction that is applied to the traffic, such as a transform (SSLtermination, VPN gateway), filter (firewalls), or terminal (intrusiondetection systems). A function within the service graph might use one ormore parameters and have one or more connectors.

The service graph may include any combination of nodes and arcs torepresent a service, topology or portions thereof. Nodes and arcs may bearranged in a manner to identify or describe the physical and/or logicaldeployment of the service and any elements used to access the service.Nodes and arcs may be arranged in a manner to identify or describe theflow of network traffic in accessing or using a service. Nodes and arcsmay be arranged in a manner to identify or describe the components of aservice, such as multiple microservices that communicate with each otherto provide functionality of the service. The service graph may be storedin storage such as a database in a manner in order for the service graphgenerator to generate a service graph in memory and/or render theservice graph in display form 514.

The service graph generator 512 may include an application, program,library, script, service, process, task or any type and form ofexecutable instructions for establishing, creating, generating,implementing, configuring or updating a service graph 505. The servicegraph generator may read and/or write data representing the servicegraph to a database, file or other type of storage. The service graphgenerator may comprise logic, functions and operations to construct thearrangement of nodes and arcs to have an electronic representation ofthe service graph in memory. The service graph generator may read oraccess the data in the database and store data into data structures andmemory elements to provide or implement a node based representation ofthe service graph that can be updated or modified. The service graphgenerator may use any information from the topology to generate aservice graph. The service graph generator may make network calls or usediscovery protocols to identify the topology or any portions thereof.The service graph generator may use any metrics, such as in memory orstorage or from other devices, to generate a service graph. The servicegraph generator may comprise logic, functions and operations toconstruct the arrangement of nodes and arcs to provide a graphical orvisual representation of the service graph, such as on a user interfaceof a display device. The service graph generator may comprise logic,functions and operations to configure any node or arc of the servicegraph to represent a configuration or parameter of the corresponding orunderlying element represented by the node or arc. The service graphgenerator may comprise logic, functions and operations to include,identify or provide metrics in connection with or as part of thearrangement of nodes and arcs of the service graph display. The servicegraph generator may comprise an application programming interface (API)for programs, applications, services, tasks, processes or systems tocreate, modify or interact with a service graph.

The service graph display 514 may include any graphical or electronicrepresentation of a service graph 505 for rendering or display on anytype and form of display device. The service graph display may berendered in visual form to have any type of color, shape, size or othergraphical indicators of the nodes and arcs of the service graph torepresent a state or status of the respective elements. The servicegraph display may be rendered in visual form to have any type of color,shape, size or other graphical indicators of the nodes and arcs of theservice graph to represent a state or status of one or more metrics. Theservice graph display may comprise any type of user interface, such as adashboard, that provides the visual form of the service graph. Theservice graph display may include any type and form of user interfaceelements to allow users to interact, interface or manipulate a servicegraph. Portion of the service graph display may be selectable toidentify information, such as metrics or topology information about thatportion of the service graph. Portions of the service graph display mayprovide user interface elements for users to take an action with respectto the service graph or portion thereof, such as to modify aconfiguration or parameter of the element.

The service graph monitor 518 may include an application, program,library, script, service, process, task or any type and form ofexecutable instructions to receive, identify, process metrics 518 of thetopology 510. The service graph monitor 518 monitors via metrics 518 theconfiguration, performance and operation of elements of a service graph.The service graph monitor may obtain metrics from one or more devices onthe network. The service graph monitor may identify or generate metricsfrom network traffic traversing the device(s) of the service graphmonitor. The service graph monitor may receive reports of metrics fromany of the elements of the topology, such as any elements represented bya node in the service graph. The service graph monitor may receivereports of metrics from the service. From the metrics, the service graphmonitor may determine the state, status or condition of an elementrepresented in or by the service graph, such as by a node of the servicegraph. From the metrics, the service graph monitor may determine thestate, status or condition of network traffic or network connectedrepresented in or by the service graph, such as by an arc of the servicegraph. The service graph generator and/or service graph monitor mayupdate the service graph display, such as continuously or inpredetermined frequencies or event based, with any metrics or anychanged in the state, status or condition of a node or arc, elementrepresented by the node or arc, the service, network or network traffictraversing the topology.

The metrics 518, 518′ (generally referred to as metrics 518) may bestored on network device in FIG. 5B, such as in memory or storage. Themetrics 518, 518′ may be stored in a database on the same device or overa network to another device, such as a server. Metrics may include anytype and form of measurement of any element of the topology, service ornetwork. Metrics may include metrics on volume, rate or timing ofrequests or responses received, transmitted or traversing the networkelement represented by the node or arc. A Metrics may include metrics onusage of a resource by the element represented by the node or arc, suchas memory, bandwidth. Metrics may include metrics on performance andoperation of a service, including any components or microservices of theservice, such as rate of response, transaction responses and times.

FIG. 5B illustrates an implementation of a service graph in connectionwith microservices of a service in view of east-west network traffic andnorth-south network traffic. In brief overview, clients 102 may accessvia one or more networks 104 a data center having servers 106A-106N(generally referred to as servers 106) providing one or more services275A-275N (generally referred to as services 275). The services may bemade up multiple microservices 575A-575N (generally referred to asmicroservice or micro service 575). Service 275A may includemicroservice 575A and 575N while service 275B may include microservice575B and 575N. The microservices may communicate among the microservicesvia application programming interface (APIs). A service graph 505 mayrepresent a topology of the services and metrics on network traffic,such as east-west network traffic and north-south network traffic.

North-south network traffic generally describes and is related tonetwork traffic between clients and servers, such as client via networks104 to servers of data center and/or servers to clients via network 104as shown in FIG. 5B. East-west network traffic generally describes andis related to network traffic between elements in the data centers, suchas data center to data center, server to server, service to service ormicroservice to microservice.

A service 275 may comprise microservices 575. In some aspects,microservices is a form of service-oriented architecture style whereinapplications are built as a collection of different smaller servicesrather than one whole or singular application (referred to sometimes asa monolithic application). Instead of a monolithic application, aservice has several independent applications or services (e.g.,microservices) that can run on their own and may be created usingdifferent coding or programming languages. As such, a larger server canbe made up of simpler and independent programs or services that areexecutable by themselves. These smaller programs or services are groupedtogether to deliver the functionalities of the larger service. In someaspects, a microservices based service structures an application as acollection of services that may be loosely coupled. The benefit ofdecomposing a service into different smaller services is that itimproves modularity. This makes the application or service easier tounderstand, develop, test, and be resilient to changes in architectureor deployment.

A microservice includes an implementation of one or more functions orfunctionality. A microservice may be a self-contained piece of businessfunction(s) with clear or established interfaces, such as an applicationprogramming interface (API). In some implementations, a microservice maybe deployed in a virtual machine or a container. A service may use oneor more functions on one microservice and another one or more functionsof a different microservice. In operating or executing a service, onemicroservice may make API calls to another microservice and themicroservice may provide a response via an API call, event handler orother interface mechanism. In operating or executing a microservice, themicroservice may make an API call to another microservice, which in itsoperation or execution, makes a call to another microservice, and so on.

The service graph 505 may include multiple nodes 570A-N connected orlinked via one or more or arcs 572A-572N. The service graph may havedifferent types of nodes. A node type may be used to represent aphysical network element, such as a server, client, appliance or networkdevice. A node type may be used to represent an end point, such as aclient or server. A node type may be used to represent an end pointgroup, such as group of clients or servers. A node type may be used torepresent a logical network element, such as a type of technology,software or service or a grouping or sub-grouping of elements. A nodetype may be used to represent a functional element, such asfunctionality to be provided by an element of the topology or by theservice.

The configuration and/or representation of any of the nodes 570 mayidentify a state, a status and/or metric(s) of the element representedby the node. Graphical features of the node may identify or specify anoperational or performance characteristic of the element represented bythe node. A size, color or shape of the node may identify an operationalstate of whether the element is operational or active. A size, color orshape of the node may identify an error condition or issue with anelement. A size, color or shape of the node may identify a level ofvolume of network traffic, a volume of request or responses received,transmitted or traversing the network element represented by the node. Asize, color or shape of the node may identify a level of usage of aresource by the element represented by the node, such as memory,bandwidth, CPU or storage. A size, color or shape of the node mayidentify relativeness with respect to a threshold for any metricassociated with the node or the element represented by the node.

The configuration and/or representation of any of the arcs 572 mayidentify a state, status and/or metric(s) of the element represented bythe arc. Graphical features of the arc may identify or specify anoperational or performance characteristic of the element represented bythe arc. A size, color or shape of the node may identify an operationalstate of whether the network connection represented by the arc isoperational or active. A size, color or shape of the arc may identify anerror condition or issue with a connection associated with the arc. Asize, color or shape of the arc may identify an error condition or issuewith network traffic associated with the arc. A size, color or shape ofthe arc may identify a level of volume of network traffic, a volume ofrequest or responses received, transmitted or traversing the networkconnection or link represented by the arc. A size, color or shape of thearc may identify a level of usage of a resource by network connection ortraffic represented by the arc, such as bandwidth. A size, color orshape of the node may identify relativeness with respect to a thresholdfor any metric associated with the arc. In some implementations, ametric for the arc may include any measurement of traffic volume perarc, latency per arc or error rate per arc.

Referring now to FIG. 5C, an implementation of a method for generatingand displaying a service graph will be described. In brief overview ofmethod 580, at step 582, a topology is identified, such as for aconfiguration of one or more services. At step 584, the metrics ofelements of the topology, such as for a service are monitored. At step586, a service graph is generated and configured. At step 588, a servicegraph is displayed. At step 590, issues with configuration, operationand performance of a service or the topology may be identified ordetermined.

At step 582, a device identifies a topology for one or more services.The device may obtain, access or receive the topology 510 from storage,such as a database. The device may be configured with a topology for aservice, such as by a user. The device may discover the topology orportions therefore via one more discovery protocols communicated overthe network. The device may obtain or receive the topology or portionsthereof from one or more other devices via the network. The device mayidentify the network elements making up one or more services. The devicemay identify functions providing the one or more services. The devicemay identify other devices or network elements providing the functions.The device may identify the network elements for north-west traffic. Thedevice may identify the network elements for east-west traffic. Thedevice may identify the microservices providing a service. In someimplementations, the service graph generator establishes or generates aservice graph based on the topology. The service graph may be stored tomemory or storage.

At step 584, the metrics of elements of the topology, such as for aservice are monitored. The device may receive metrics about the one ormore network elements of the topology from other devices. The device maydetermine metrics from network traffic traversing the device. The devicemay receive metrics from network elements of the topology, such as viareports or events. The device may monitor the service to obtain orreceive metrics about the service. The metrics may be stored in memoryor storage, such as in association with a corresponding service graph.The device may associate one or more of the metrics with a correspondingnode of a service graph. The device may associate one or more of themetrics with a corresponding arc of a service graph. The device maymonitor and/or obtain and/or receive metrics on a scheduled orpredetermined frequency. The device may monitor and/or obtain and/orreceive metrics on a continuous basis, such as in real-time ordynamically when metrics change.

At step 586, a service graph is generated and configured. A servicegraph generator may generate a service graph based at least on thetopology. A service graph generator may generate a service graph basedat least on a service. A service graph generator may generate a servicegraph based on multiple services. A service graph generator may generatea service graph based at least on the microservices making up a service.A service graph generator may generate a service graph based on a datacenter, servers of the data center and/or services of the data center. Aservice graph generator may generate a service graph based at least oneast-west traffic and corresponding network elements. A service graphgenerator may generate a service graph based at least on north-southtraffic and corresponding network elements. A service graph generatormay configure the service graph with parameters, configuration data ormeta-data about the elements represented by a node or arc of the servicegraph. The service graph may be generated automatically by the device.The service graph may be generated responsive to a request by a user,such as via a comment to or user interface of the device.

At step 588, a service graph is displayed. The device, such as viaservice graph generator, may create a service graph display 514 to bedisplayed or rendered via a display device, such as presented on a userinterface. The service graph display may include visual indicators orgraphical characteristics (e.g., size, shape or color) of the nodes andarcs of the service graph to identify status, state or condition ofelements associated with or corresponding to a node or arc. The servicegraph display may be displayed or presented via a dashboard or otheruser interface in which a user may monitor the status of the service andtopology. The service graph display may be updated to show changes inmetrics or the status, state and/or condition of the service, thetopology or any elements thereof. Via the service graph display, a usermay interface or interact with the service graph to discoverinformation, data and details about any of the network elements, such asthe metrics of a microservice of a service.

At step 590, issues with configuration, operation and performance of aservice or the topology may be identified or determined. The device maydetermine issues with the configuration, operation or performance of aservice by comparing metrics of the service to thresholds. The devicemay determine issues with the configuration, operation or performance ofa service by comparing metrics of the service to previous or historicalvalues. The device may determine issues with the configuration,operation or performance of a service by identifying a change in ametric. The device may determine issues with the configuration,operation or performance of a service by identifying a change in astatus, state or condition of a node or arc or elements represented bythe node or arc. The device may change the configuration and/orparameters of the service graph. The device may change the configurationof the service. The device may change the configuration of the topology.The device may change the configuration of network elements making upthe topology or the service. A user may determine issues with theconfiguration, operation or performance of a service by reviewing,exploring or interacting with the service graph display and any metrics.The user may change the configuration and/or parameters of the servicegraph. The user may change the configuration of the service. The usermay change the configuration of the topology. The device may change theconfiguration of network elements making up the topology or the service.

F. Systems and Methods for Using Service Graphs to Compare Performanceof a Plurality of Versions of a Microservice

Systems and methods for using service graphs to compare performance of aplurality of versions of a microservice are discussed herein. Asdescribed above, a service graph may be a tool by which a serviceincluding various microservices corresponding thereto may be visualized.Such a tool may be used for network traffic monitoring purposes,diagnostic purposes, troubleshooting purposes, and so forth. The servicegraph may depict various metrics corresponding to network conditions andtopology (e.g., traffic volume, latency, error rates, and other metricscorresponding to the service). In some implementations, such as duringrollout of a new version of a microservice, a user may want to monitorperformance of the new version of the microservice. For instance, theuser may want to monitor metrics of the new version of the microservicein comparison to the previous version of the microservice—particularlywhere the deployment of the new version is a canary deployment.

A device can monitor metrics from execution of a plurality of versionsof a microservice corresponding to a service. The versions may bedeployed concurrently for a portion of execution of the service (e.g.,as part of a canary deployment, for instance). The device can generateservice graphs of each version of the microservice which include themonitored metrics. The device can identify differences in metricsbetween the service graphs of the different versions. The device canrequest a change in network traffic of the service between the versionsof the microservice based on the identified differences in metrics. Assuch, the device can determine whether the deployment of a new versionof a microservice is performing as intended and gradually divert networktraffic to the new version while phasing out the previous version of themicroservice.

Referring now to FIG. 6A, depicted is an example service graph 505showing concurrent deployment of a microservice 575A (e.g., microservice575A1 and microservice 575A2). As shown in FIG. 6A, the service graph505 may include nodes 570A-570E corresponding to respectivemicroservices 575A-575N. The service graphs 505 may depict variousmetrics. For instance, each service graph 505 may include metricscorresponding to the particular time at which the service graph 505 wasgenerated, produced, configured, displayed, etc. (e.g., by the servicegraph generator and configurator 512 as described above). The servicegraph 505 may represent real-time metrics of the microservices 575.

Referring now to FIG. 5A and FIG. 6A, as described in greater detailabove in Section E, the service graph 505 includes any combination ofnodes 570A-N and arcs 572A-N that represent a service, topology, orportions thereof. Nodes 570A-N and arcs 572A-N may be arranged toidentify or describe the physical and/or logical deployment of theservice (e.g., including microservices 575 corresponding to theservice), identify or describe the flow of network traffic during accessor use of the service, and/or any elements used to access the service.The service graph generator 512 may read and/or write data representingthe service graph 505 to a database 520 for use or display, as describedin greater detail below. The service graph monitor 516 may be configuredto receive, identify, and process metrics 518 of the topology 510corresponding to the service graph 505. The service graph monitor 516monitors via metrics 518 the configuration, performance and operation ofelements of a service graph. The service graph monitor 516 may obtainmetrics from one or more devices on the network. The service graphmonitor 516 may identify or generate metrics, such as network trafficrate or flow, latency, error rate, etc. from network traffic traversingthe device(s) monitored by the service graph monitor 516. The servicegraph display 514 may be configured to update the service graph 505(e.g., in real-time) to reflect the metrics identified and/or generatedby the service graph monitor 516, as described in greater detail abovein Section E.

In some embodiments, the network device 200, 205, 106 (generallyreferred to as device 200) may be configured to control rollout ordeployment of various versions of microservices 575. The device 200 maybe configured to receive new versions of microservices from a computingdevice via the network 104. A user may update, refine, modify, orotherwise generate new versions of microservices 575 from time to time.The user may generate new versions of the microservice 575 and transmitthe new version of the microservice 575 to the device 200 (e.g., viatheir respective computing device across the network 104) forincorporation into the service. Rather than deploying the new version ofthe microservice 575 all at once and risking interruptions, delays, orother anomalies in performance, the device 200 may phase in the newversion of the microservice 575 and phase out the old version of themicroservice 575. In so doing, the device 200 may monitor metrics of thenew version of the microservice 575 in comparison to the old version ofthe microservice to determine that the new version of the microservice575 is performing as intended. This approach and technique correspondsto a canary deployment. The device 200 may thus deploy the versions ofthe microservice 575 concurrently, and monitor metrics of the service(including the microservices 575) while the versions of themicroservices 575 are deployed concurrently. The device 200 maygradually phase in the new version of the microservice 575 as the newversion of the microservice 575 is shown to perform as intended.

As shown in FIG. 6A, the service 275A includes a first version ofmicroservice 575A1 and a second version of microservice 575A2. The firstand second versions of microservice 575A may be represented in theservice graph 505 by respective nodes 570A1 and 575A2. The device 200may be configured to deploy the first and second versions 575A1, 575A2of the microservice 575A via a canary deployment. The canary deploymentmay be used for deploying a new version (e.g., the second version of themicroservice 575A2) for a subset of users of the service 275A whiledeploying the previous version (e.g., the first version of themicroservice 575A2) for the remaining users of the service 275A. Thedevice 200 may deploy the new and old version of the microservice 575A1,575A2 via a canary deployment by distributing a percentage of networktraffic to the first version of the microservice 575A1 and a percentageof network traffic to the second version of the microservice 575A2. Asdescribed in greater detail below, the device 200 may modify thepercentages based on monitored metrics for the respective versions ofthe microservice 575A1, 575A2.

The device 200 may be configured to monitor, generate, identify,determine, or otherwise establish metrics from execution of each versionof the microservice 575A1, 575A2. In some embodiments, the service graphgenerator 512 may be configured to generate a service graph 505representing each of the versions of the microservice 575A1, 575A2.Specifically, FIG. 6B and FIG. 6C show service graphs 505 includingrespective versions of the microservice 575A1, 575A2. The service graphmonitor 516 may be configured to monitor metrics from execution of eachof the versions of the microservice 575A. The service graph generator512 may be configured to generate the service graphs 505 shown in FIG.6A and FIG. 6B based on the monitored metrics. The device 200 may usethe service graphs 505 for controlling deployment of the versions of themicroservice 575A, as described in greater detail below.

The service graph monitor 516 may be configured to establish variousmetrics corresponding to the configuration, operation, status, topology,deployment, condition, state, or performance of the versions of themicroservice 575A. The service graph monitor 516 may monitor the metricsfor the performance of the service 275 while the respective versions ofthe microservice 575A are deployed. As described above in Section E, theservice graph monitor 516 may be configured to monitor metrics, such astraffic volume, latency, error rates, and so forth between two or moremicroservices 575 of a service 275. The service graph monitor 516 maymonitor the metrics corresponding to each version of the microservice575 (e.g., metrics between the first version of the microservice 575A1and other microservices 575 and metrics between the second version ofthe microservice 575A2 and other microservices 575A).

The service graph generator 512 may be configured to generate servicegraphs 505 to reflect, identify, or otherwise include the metricsmonitored by the service graph monitor 516 and corresponding to theexecution of the respective versions of the microservice 575A. Theservice graph generator 512 may be configured to generate the servicegraphs 505 to reflect the metrics. The service graph generator 512 maybe configured to adjust one or more aspects of the service graphs 505based on the metrics.

As shown in FIG. 6B and FIG. 6C, each of the service graphs 505 mayinclude nodes 570 representing the respective versions of themicroservices 575A1, 575A2. The service graphs 505 may include arcs 572representing connections between the versions of the microservice 575A1,575A2 and other microservices 575. The nodes 570 and arcs 572 may bemodified based on the metrics 518 corresponding to the execution of therespective versions of the microservices 575A1, 575A2.

The service graph monitor 516 may be configured to detect differencesbetween the execution, implementation, or otherwise operation of theversions of the microservice 575A based on differences in thecorresponding service graphs 505. The service graph monitor 516 maymonitor metrics (e.g., network traffic volume, error rates, latency,etc.) of the versions of the microservice 575A1, 575A2 continuously,near-continuously, at the time at which the service graph 505 isgenerated, etc. The service graph monitor 516 may compare the metricsfor the service graph 505 corresponding to execution of the firstversion of the microservice 575A1 with the metrics for the service graph505 corresponding to the execution of the second version of themicroservice 575A2. The service graph monitor 516 may determine whetherthere are any differences between the service graphs 505 based on thecomparison.

The service graph monitor 516 may be configured to determine the trafficvolume for the versions of the microservices 575A1, 575A2. The trafficvolume may be represented within the service graph via the arcs 572A,572B. For instance, where traffic volume is increased to the respectiveversions of the microservice 575A1, 575A2, the arc 575 may be bolded,shortened (or lengthened), etc. The traffic volume may be representedwithin the service graph via any type and form of user interfaceelements, such as an overlay on top of (or adjacent to) the arcs 572A,572B. For instance, the overlay may indicate the traffic volume of therespective version of the microservice 575A1, 575A2. The service graphmonitor 516 may be configured to compare the traffic volume for theservice graph 505 corresponding to execution of the first version of themicroservice 575A1 with the traffic volume for the service graph 505corresponding to execution of the second version of the microservice575A2.

The service graph monitor 516 may be configured to determine the errorrate of the versions of the microservices 575A1, 575A2. The error ratemay be represented within the service graph via the arcs 572A, 572B. Forinstance, where the error rate increases, the arc 575 may be a brokenline (e.g., dotted, dashed, etc.), lengthened, etc. The error rate maybe represented within the service graph via any type and form of userinterface elements, such as an overlay on top of (or adjacent to) thearcs 572A, 572B. For instance, the overlay may indicate the error rateof the respective version of the microservice 575A1, 575A2. The servicegraph monitor 516 may be configured to compare the error rate for theservice graph 505 corresponding to execution of the first version of themicroservice 575A1 with the error rate for the service graph 505corresponding to execution of the second version of the microservice575A2.

The service graph monitor 516 may be configured to determine the latencyof the versions of the microservices 575A1, 575A2. The latency may berepresented within the service graph via the arcs 572A, 572B. Forinstance, where latency increases, the arc 575 may be a broken line(e.g., dotted, dashed, etc.), lengthened, etc. The latency may berepresented within the service graph via a text overlay on top of (oradjacent to) the arcs 572A, 572B. For instance, the text overlay mayindicate the latency for the respective version of the microservice575A1, 575A2. The service graph monitor 516 may be configured to comparethe latency for the service graph 505 corresponding to execution of thefirst version of the microservice 575A1 with the latency for the servicegraph 505 corresponding to execution of the second version of themicroservice 575A2.

The service graph monitor 516 may determine a state of the versions ofthe microservice 575A1, 575A2 (e.g., active/partially active/inactivestate). The service graph monitor 516 may be configured to identifychanges in the state of the versions of the microservice 575A1, 575A2(e.g., a change from active to inactive, a change from active topartially active, etc.). The versions of the microservices 575A1, 575A2may automatically change their state upon occurrence of an anomaly(e.g., when network traffic falls below a threshold, when error ratesexceed a threshold, latency exceeds a threshold, etc.). The state of theversions of the microservice 575A1, 575A2 may be reflected in theservice graph 505. In some embodiments, the state may be representedwithin the service graph via the arcs 572A, 572B. For instance, the arc572 may be a solid line where state of the microservice 575A is active,the arc 572 may be a broken line where the state of the microservice575A is partially active, and the arc 572 may be removed where the stateof the microservice 575A is inactive. In some embodiments, the state maybe represented within the service graph 505 via the nodes 570A. Forinstance, the service graph generator 512 may be configured to modify asize, color, opacity, line format, etc. of the nodes 570 based on thestate of the microservices 575. The service graph generator 512 maychange the size, color, opacity, line format, etc. as the state changesbetween active, partially active, and inactive (e.g., shrink the size ofthe node 570, change the color of the node 570 from green to yellow tored, decrease the opacity, break the line or change the color of theline defining the node 570, respectively).

The service graph monitor 516 may be configured to identify differencesbetween the service graphs 505 corresponding to the respective versionsof the microservice 575A1, 575A2. The service graph monitor 516 may beconfigured to compare the service graphs 505 corresponding to therespective versions of the microservice 575A1, 575A2 for identifyingdifferences in the execution of the versions of the microservice 575A1,575A2. The service graph monitor 516 may identify differences in networktraffic rates, latency, error rates, etc. from the execution of theversions of the microservice 575A1, 575A2 based on the comparison of therespective service graphs 505. In some instances, the service graphmonitor 516 may identify decreases in error rate, decreases in latency,etc. of the second (e.g., new) version of the microservice 575A2 incomparison to the error rate, latency, etc. of the first (e.g.,previous) version of the microservice 575A1. In some instances, theerror rate and latency may be substantially the same. In these and otherinstances, the new version of the microservice 575A2 may be performingas intended (e.g., the same as or better performance and metrics thanthe previous version of the microservice 575A1). In some instances,however, the service graph monitor 516 may identify increases in errorrate, increases in latency, etc. of the second (e.g., new) version ofthe microservice 575A2 in comparison to the error rate, latency, etc. ofthe first (e.g., previous) version of the microservice 575A1. In suchinstances, the new version of the microservice 575A2 may not beperforming as intended (e.g., worse performance and metrics than theprevious version of the microservice 575A2).

The device 200 may be configured to modify deployment of the first andsecond version of the microservice 575A2 based on the differencesbetween the service graphs 505. The device 200 may be configured torequest a change in network traffic of the service 275 between the firstand second version of the microservice 575A1, 575A2 based on theidentified differences in the service graphs 505. The device 200 may beconfigured to modify, change, or otherwise adjust the percentage ofnetwork traffic distributed to the first and second versions of themicroservice 575A1, 575A2 based on the differences in the service graphs505. For instance, the device 200 may be configured to increase thepercentage (or portion) of the network traffic distributed to the newversion of the microservice 575A2 while correspondingly decreasing thepercentage (or portion) of the network traffic distributed to theprevious version of the microservice 575A1. In other words, the device200 may shift, divert, or otherwise route a portion of network trafficfrom the previous version of the microservice 575A1 to the new versionof the microservice 575A2. The device 200 may increase the percentage ofthe network traffic allocated to the new version of the microservice575A2 when the differences between the service graphs 505 indicate thenew version of the microservice 575A2 is performing as intended (e.g.,the same as or better performance and metrics than the previous versionof the microservice 575A1). On the other hand, the device 200 mayincrease the percentage (or portion) of the network traffic distributedto the previous version of the microservice 575A1 while correspondinglyincreasing the percentage (or portion) of the network trafficdistributed to the new version of the microservice 575A2. The device 200may decrease the percentage of the network traffic allocated to the newversion of the microservice 575A2 when the differences between theservice graphs 505 indicate the new version of the microservice 575A2 isnot performing as intended (e.g., worse performance and metrics than theprevious version of the microservice 575A2).

Accordingly, the device 200 may control deployment of new versions of amicroservice 575 by concurrently deploying a new and old version of themicroservice 575 during execution of a service 275, monitor metricsrepresented within a service graph corresponding to the respectiveversions, and allocate more (or less) network traffic to the respectiveversions of the microservice 575 based on the differences between theservice graphs 505. The device 200 may gradually phase in the newversion of the microservice 575 as the new version of the microservice575 is shown to perform as intended.

Referring now to FIG. 6B, an implementation of a method 600 for usingservice graphs to compare performance of a plurality of versions of amicroservice will be described. In brief overview of method 600, at step605, a device deploys versions of a microservice. At step 610, thedevice establishes metrics from execution of the versions of themicroservice. At step 615, the device generates service graphs of theversions of the microservice. At step 620, the device identifiesdifferences between the first and second service graphs. At step 625,the device requests a change in network traffic.

At step 605, a device deploys versions of a microservice. In someembodiments, the device may deploy a plurality of versions of amicroservice for a service. The microservice may be one of a pluralityof microservice for the service. The device may deploy the versions ofthe microservice concurrently for a portion of execution of the service.The device may deploy a new and an old version of the same microservice.The device may deploy the new and old version of the microserviceresponsive to receiving an update (e.g., from a developer via theircomputing device across a computer network). The device may deploy theversions of the microservice via a canary deployment. In someembodiments, the device may allocate, apportion, or otherwise distributea first percentage of network traffic to the first version (e.g., theold version) of the microservice and a second percentage of networktraffic to the second version (e.g., the new version) of themicroservice. The device may modify the percentage or portion of networktraffic distributed to the respective versions based on monitoredmetrics corresponding to execution of the versions of the microservice.

At step 610, the device establish metrics from execution of the versionsof the microservice. In some embodiments, the device may establishmetrics from execution of the versions of the microservice (e.g.,deployed at step 605). In some embodiments, step 610 may be similar insome respects to step 584 of FIG. 5C. The device may establish metricscorresponding to the execution of each of the versions of themicroservice for the service. The device may establish the metrics whilethe versions of the microservice are deployed concurrently. The devicemay establish metrics corresponding to the network traffic, latency,error rate, state, etc. for each of the versions of the microservice.

At step 615, the device generates service graphs of the versions of themicroservice. In some embodiments, the device may generate a servicegraph for each version of the plurality of versions of the microservice(e.g., deployed at step 605). Each of the service graphs may includemetrics from execution of a respective version of the microservice. Insome embodiments, the device may generate a first service graph of afirst version of the plurality of versions of the microservice. Thefirst service graph may include the established metrics from executionof the versions of the microservice (e.g., at step 610). Step 615 may besimilar in some respects to step 586 of FIG. 5C. The service graphs mayinclude various features, aspects, etc. which indicate or otherwisecorrespond to the metrics from execution of the respective version ofthe microservice within the service.

In some embodiments, the device may generate a first service graph for afirst version of the microservice, a second service graph for a secondversion of the microservice, etc. The first service graph may includenodes representing each (or a subset) of the microservices for theservice including the first version of the microservice. In someembodiments, the nodes may be modified/adapted to represent a state ofthe corresponding microservices (e.g., change in color, line breaks,size, opacity, etc. corresponding to changes between an active,partially active, and inactive state). The first service graph mayindicate, or otherwise correspond to the topology of the service andsupporting devices. The first service graph may include an arc betweenthe first version of the microservice and other microservice(s) of theservice. The arc may identify one or more of the monitored metric(s)(e.g., at step 610). The arc may identify the monitored metrics throughchanging the shape, format, etc. of the arc, changing the length of thearc, through a text overlay on top of or adjacent to the arc, and soforth. The arc may identify the traffic volume between at least two ofthe microservices for the service, latency between at least two of themicroservices for the service, error rate between at least two of themicroservices for the service, and so forth. The device may use thefirst service graph for determining performance of the first version ofthe microservice relative to the second version of the microservice forcontrolling deployment of the versions of the microservice, as describedin greater detail below.

Similarly, the device may generate a second service graph of a secondversion of the microservice. The second service graph may includemetrics from monitoring execution of the second version of themicroservice. The second service graph may be similar in some aspects tothe first service graph. However, any differences between the first andsecond service graph may be a result of or correlate to difference inmetrics corresponding to execution of the first and second version ofthe microservice. The first and second service graphs may correspond tosubstantially the same time. Hence, the first and second service graphmay represent the topology, performance, or other characteristics of theservice while the respective versions of the microservice are executingconcurrently.

At step 620, the device identifies differences between service graphs.In some embodiments, the device identifies one or more differences inmetrics between the respective service graphs (e.g., generated at step615). The device may compare the network traffic, latency, error rate,state, etc. represented or otherwise included in the respective servicegraphs. The device may compare the network traffic, latency, error rate,state, etc. represented or otherwise included in the first service graphwith the network traffic, latency, error rate, state, etc. included inthe second (and/or third, and so forth) service graph. The device mayidentify differences between the respective service graphs based on thecomparison of the metrics. In some instances, the device may determinewhether one of the versions (e.g., new version) of the microservice isperforming as intended based on the comparison. For instance, the devicemay determine whether the differences show the execution of the newversion of the microservice resulted in decreased (or the same) errorrate, decreased (or the same) latency, no changes in state of any of themicroservices, etc. The device may determine the new version isperforming as intended based on the service graph corresponding to thenew version having metrics showing performance which is the same as oran improvement of the metrics of a service graph corresponding to aprevious version. On the other hand, the device may determine the newversion is not performing as intended where the difference show theexecution of the new version of the microservice resulted in increasederror rate, increased latency, one or more state changes of themicroservice(s) for the service, etc.

At step 630, the device requests a change in network traffic. In someembodiments, the device may request a change in network traffic of theservice between the versions of the microservice based at least on theone or more differences (e.g., identified at step 625). The device mayrequest the change in network traffic based on whether or not the newversion of the microservice is determined to be performing as intended.The device may request a change in network traffic to change thepercentage or portion of network traffic allocated to the new version ofthe microservice based on the comparison of the service graphs.

In some instances (such as where the differences indicate the secondversion is performing as intended), the device may increase over timethe percentage (or portion) of network traffic distributed to a second(e.g., new) version of the microservice while correspondingly decreasingthe second percentage (or portion) of the network traffic distributed toa first (e.g., old) version of the microservice. As such, the device maygradually phase in the second version of the microservice whilecorrespondingly phasing out the first version of the microservice basedon the metrics corresponding to the respective versions of themicroservice. The device may request to switch a portion of the networktraffic from the first version of the microservice to the second versionof the microservice based on the differences, effectively re-allocatingor shifting some of the network traffic of the first version to thesecond version of the microservice. On the other hand, where the secondversion of the microservice is not performing as intended (e.g., basedon the difference in metrics corresponding to the execution of therespective versions), the device may rollback the second version of themicroservice. The device may increase over time the percentage (orportion) of network traffic distributed to the first version of themicroservice while correspondingly decreasing the second percentage (orportion) of the network traffic distributed to the second version of themicroservice. Such implementations and embodiments provide for aneffective and efficient way of rolling out versions of a microservicethrough the use of service graphs.

Various elements, which are described herein in the context of one ormore embodiments, may be provided separately or in any suitablesubcombination. For example, the processes described herein may beimplemented in hardware, software, or a combination thereof. Further,the processes described herein are not limited to the specificembodiments described. For example, the processes described herein arenot limited to the specific processing order described herein and,rather, process blocks may be re-ordered, combined, removed, orperformed in parallel or in serial, as necessary, to achieve the resultsset forth herein.

It will be further understood that various changes in the details,materials, and arrangements of the parts that have been described andillustrated herein may be made by those skilled in the art withoutdeparting from the scope of the following claims.

1-20. (canceled)
 21. A method comprising: establishing, by one or moreprocessors, a service graph for different versions of a service based atleast on one or more metrics of network traffic for the differentversions, and the different versions being executable concurrently;identifying, by the one or more processors, a difference in one or moremetrics between a plurality of versions of the service using at leastone service graph for the different versions; and causing, by the one ormore processors, a change in network traffic of at least one version ofthe service.
 22. The method of claim 21, wherein each version of theplurality of services comprises at least one microservice common betweeneach version.
 23. The method of claim 22, wherein each version of theplurality of versions comprises a different version of the at least onemicroservice.
 24. The method of claim 22, further comprisingestablishing, by the one or more processors, the service graph fordifferent versions of the service based at least on one or more metricsof the portions of network traffic received by the at least onemicroservice.
 25. The method of claim 21, further comprisingestablishing the service graph for different versions of the servicebased at least on each version of the different versions beingdistributed a percentage of the network traffic for the service.
 26. Themethod of claim 21, further comprising, causing, by the one or moreprocessors, a change in a percentage of network traffic distributedbetween the different versions.
 27. The method of claim 21, furthercomprising communicating, by the one or more processors, a request to acomputing device to change the portion of network traffic distributed tothe at least one service.
 28. A system comprising: one or moreprocessors, coupled to memory and configured to: establish a servicegraph for different versions of a service based at least on one or moremetrics of network traffic for the different versions, and the differentversions being executable concurrently; identify a difference in one ormore metrics between a plurality of versions of the service using atleast one service graph for the different versions; and cause a changein network traffic of at least one version of the service.
 29. Thesystem of claim 28, wherein each version of the plurality of servicescomprises at least one microservice common between each version.
 30. Thesystem of claim 29, wherein each version of the plurality of versionscomprises a different version of the at least one microservice.
 31. Thesystem of claim 29, wherein the one or more processors are furtherconfigured to establish the service graph for different versions of theservice based at least on one or more metrics of the portions of networktraffic received by the at least one microservice.
 32. The system ofclaim 28, wherein the one or more processors are further configured toestablish the service graph for different versions of the service basedat least on each version of the different versions being distributed apercentage of the network traffic for the service.
 33. The system ofclaim 28, wherein the one or more processors are further configured tocause a change in a percentage of network traffic distributed betweenthe different versions.
 34. The system of claim 28, wherein the one ormore processors are further configured to communicate a request to acomputing device to change the portion of network traffic distributed tothe at least one service.
 35. A system comprising: one or moreprocessors, coupled to memory and configured to: identify one or moremetrics of different versions of a service being executed concurrently,each of different versions comprising a different version of amicroservice; determine using the one or more metrics a differencebetween the microservice of different versions of the service; and causea change in a portion of network traffic to be received by at least onversion of the service.
 36. The system of claim 35, wherein the one ormore processors are further configured to establish, using the one ormore metrics, service graphs for each of the different versions.
 37. Thesystem of claim 36, wherein the one or more processors are furtherconfigured to determine the difference using the services graphs foreach of the different versions.
 38. The system of claim 35, wherein theone or more processors are further configured to identify the differenceusing at least one service graph for the different versions.
 39. Thesystem of claim 35, wherein the one or more processors are furtherconfigured to cause the change in the portion of network trafficreceived by the microservice of at least one version of the service. 40.The system of claim 35, wherein the one or more processors are furtherconfigured to cause a change in a percentage of network traffic receivedby the different versions.